Generating Conceptual Metaphoric Paraphrases

Metaphoric Paraphrase generation is a relatively new and unexplored Natural Language Generation task. The aim of the task is to develop computational systems that paraphrase literal sentences into cogent metaphoric ones. Challenges in the field include representation of common sense knowledge and ensuring meaning retention when dealing with phrases that are dissimilar in their literal sense. This thesis will deal with the specific task of paraphrasing literal adjective phrases into metaphoric noun phrases, taking into consideration the preceding context of the adjective phrase. Two different systems were developed as part of this study. The systems are identical, apart from the fact that one is endowed with a knowledge representation based on Conceptual Metaphor Theory. The paraphrases generated by the systems, along with paraphrases written by a native speaker of English, were scored on the parameters of meaning retention and creativity by a crowd-sourced panel. Both systems were able to generate cogent metaphoric paraphrases, although fairly unreliably compared to the human. The system endowed with Conceptual Metaphor Theory knowledge got a lower average meaning retention score and a higher average creativity score than the system without Conceptual Metaphor Theory knowledge representation. In addition to that it was found that less similarity in sentence embeddings of literal sentences and metaphoric paraphrases of them correlates with a higher level of perceived meaning retention and a lower perceived creativity of the metaphoric paraphrase. It was also found that less difference in GPT-2 log probability between literal sentences and metaphoric paraphrases of them correlates with humans evaluating the paraphrases as less creative